





'''simple docstring'''






import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def        _a (__SCREAMING_SNAKE_CASE=None	,   __SCREAMING_SNAKE_CASE=None ):


  """simple docstring"""

  return field(default_factory=lambda: default	,   metadata=__SCREAMING_SNAKE_CASE )







@dataclass
class 		UpperCAmelCase :


  """simple docstring"""







  lowerCAmelCase_											=					field(
      metadata={"""help""": """The csv file to plot."""} ,  )
  lowerCAmelCase_											=					field(
      default=lowercase_ ,  metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""} ,  )
  lowerCAmelCase_											=					field(
      default=lowercase_ ,  metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""} ,  )
  lowerCAmelCase_											=					field(
      default=lowercase_ ,  metadata={"""help""": """Disable logarithmic scale when plotting"""} ,  )
  lowerCAmelCase_											=					field(
      default=lowercase_ ,  metadata={
          """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
      } ,  )
  lowerCAmelCase_											=					field(
      default=lowercase_ ,  metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""} ,  )
  lowerCAmelCase_											=					list_field(
      default=lowercase_ ,  metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""})




def        _a (__SCREAMING_SNAKE_CASE ):


  """simple docstring"""

  try:
    int(__SCREAMING_SNAKE_CASE )
    return True
  except ValueError:
    return False


def        _a (__SCREAMING_SNAKE_CASE ):


  """simple docstring"""

  try:
    float(__SCREAMING_SNAKE_CASE )
    return True
  except ValueError:
    return False







class 		UpperCAmelCase :


  """simple docstring"""







  def __init__(					self     :       Union[str, Any]				,						UpperCamelCase__     :       str	)  ->    Any:
    _UpperCamelCase							      =args
    _UpperCamelCase							      =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

    with open(self.args.csv_file				,						newline=''''''	) as csv_file:
      _UpperCamelCase							      =csv.DictReader(UpperCamelCase__	)
      for row in reader:
        _UpperCamelCase							      =row['''model''']
        self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']	)	)
        self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']	)	)
        if can_convert_to_int(row['''result''']	):
          # value is not None
          _UpperCamelCase							      =int(row['''result''']	)
        elif can_convert_to_float(row['''result''']	):
          # value is not None
          _UpperCamelCase							      =float(row['''result''']	)




  def 	UpperCamelCase__   (					self     :       Optional[int]	)  ->    Tuple:
    _UpperCamelCase      ,  _UpperCamelCase							      =plt.subplots()
    _UpperCamelCase							      ='''Time usage''' if self.args.is_time else '''Memory usage'''
    _UpperCamelCase							      =title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

    if not self.args.no_log_scale:
      # set logarithm scales
      ax.set_xscale('''log'''	)
      ax.set_yscale('''log'''	)

    for axis in [ax.xaxis, ax.yaxis]:
      axis.set_major_formatter(ScalarFormatter()	)

    for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
      _UpperCamelCase							      =sorted(set(self.result_dict[model_name]['''bsz''']	)	)
      _UpperCamelCase							      =sorted(set(self.result_dict[model_name]['''seq_len''']	)	)
      _UpperCamelCase							      =self.result_dict[model_name]['''result''']

      ((_UpperCamelCase)      ,  (_UpperCamelCase))							      =(
          (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
      )

      _UpperCamelCase							      =(
          model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
      )

      for inner_loop_value in inner_loop_array:
        if self.args.plot_along_batch:
          _UpperCamelCase							      =np.asarray(
              [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,						dtype=UpperCamelCase__				,						)
        else:
          _UpperCamelCase							      =np.asarray(
              [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,						dtype=np.floataa				,						)

        ((_UpperCamelCase)      ,  (_UpperCamelCase))							      =(
            ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
        )

        _UpperCamelCase							      =np.asarray(UpperCamelCase__				,						UpperCamelCase__	)[: len(UpperCamelCase__	)]
        plt.scatter(
            UpperCamelCase__				,						UpperCamelCase__				,						label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
        plt.plot(UpperCamelCase__				,						UpperCamelCase__				,						'''--'''	)

      title_str += F''' {label_model_name} vs.'''

    _UpperCamelCase							      =title_str[:-4]
    _UpperCamelCase							      ='''Time in s''' if self.args.is_time else '''Memory in MB'''

    # plot
    plt.title(UpperCamelCase__	)
    plt.xlabel(UpperCamelCase__	)
    plt.ylabel(UpperCamelCase__	)
    plt.legend()

    if self.args.figure_png_file is not None:
      plt.savefig(self.args.figure_png_file	)
    else:
      plt.show()




def        _a ():


  """simple docstring"""

  _UpperCamelCase							      =HfArgumentParser(__SCREAMING_SNAKE_CASE )
  _UpperCamelCase							      =parser.parse_args_into_dataclasses()[0]
  _UpperCamelCase							      =Plot(args=__SCREAMING_SNAKE_CASE )
  plot.plot()


if __name__ == "__main__":
      main()












'''simple docstring'''







import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase_     						=				'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_     						=				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_     						=				re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_     						=				re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_     						=				re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase_     						=				[
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,UpperCAmelCase						)
   return [m.group(0						) for m in matches]







def 						_A      ( ):






   '''simple docstring'''







   A__       =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   A__       =     {
       config.replace('Config'      ,''						): model_type for model_type, config in config_maping_names.items()
   }

   # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)

   # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
   for attr_name in dir(UpperCAmelCase						):
      A__       =     None
      if _re_tf_models.match(UpperCAmelCase						) is not None:
         A__       =     tf_models
         A__       =     _re_tf_models.match(UpperCAmelCase						).groups()[0]
      elif _re_flax_models.match(UpperCAmelCase						) is not None:
         A__       =     flax_models
         A__       =     _re_flax_models.match(UpperCAmelCase						).groups()[0]
      elif _re_pt_models.match(UpperCAmelCase						) is not None:
         A__       =     pt_models
         A__       =     _re_pt_models.match(UpperCAmelCase						).groups()[0]

      if lookup_dict is not None:
         while len(UpperCAmelCase						) > 0:
            if attr_name in model_prefix_to_model_type:
               A__       =     True
               break
            # Try again after removing the last word in the name
            A__       =     ''.join(camel_case_split(UpperCAmelCase						)[:-1]						)

   A__       =     set(list(pt_models.keys()						) + list(tf_models.keys()						) + list(flax_models.keys()						)						)
   A__       =     list(UpperCAmelCase						)
   all_models.sort()

   A__       =     {'model_type': all_models}
   A__       =     [pt_models[t] for t in all_models]
   A__       =     [tf_models[t] for t in all_models]
   A__       =     [flax_models[t] for t in all_models]

   # Now let's use the auto-mapping names to make sure
   A__       =     {}
   for t in all_models:
      if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
         A__       =     'AutoProcessor'
      elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
         A__       =     'AutoTokenizer'
      elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
         A__       =     'AutoFeatureExtractor'
      else:
         # Default to AutoTokenizer if a model has nothing, for backward compatibility.
         A__       =     'AutoTokenizer'

   A__       =     [processors[t] for t in all_models]

   return pd.DataFrame(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     [
       transformers_module.models.auto.modeling_auto,
       transformers_module.models.auto.modeling_tf_auto,
       transformers_module.models.auto.modeling_flax_auto,
   ]
   for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
      A__       =     [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
      A__       =     [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
      # Loop through all three frameworks
      for module, cls, mapping in zip(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         # The type of pipeline may not exist in this framework
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						):
            continue
         # First extract all model_names
         A__       =     []
         for name in getattr(UpperCAmelCase      ,UpperCAmelCase						).values():
            if isinstance(UpperCAmelCase      ,UpperCAmelCase						):
               model_names.append(UpperCAmelCase						)
            else:
               model_names.extend(list(UpperCAmelCase						)						)

            # Add pipeline tag and auto model class for those models
         table.update({model_name: (pipeline_tag, cls) for model_name in model_names}						)

   return table







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_frameworks_table()
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   A__       =     hf_hub_download(
       'huggingface/transformers-metadata'      ,'pipeline_tags.json'      ,repo_type='dataset'      ,token=UpperCAmelCase						)
   A__       =     Dataset.from_json(UpperCAmelCase						)
   A__       =     {
       tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
       for i in range(len(UpperCAmelCase						)						)
   }
   A__       =     update_pipeline_and_auto_class_table(UpperCAmelCase						)

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   A__       =     sorted(table.keys()						)
   A__       =     pd.DataFrame(
       {
           'model_class': model_classes,
           'pipeline_tag': [table[m][0] for m in model_classes],
           'auto_class': [table[m][1] for m in model_classes],
       }						)
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   with tempfile.TemporaryDirectory() as tmp_dir:
      frameworks_dataset.to_json(os.path.join(UpperCAmelCase      ,'frameworks.json'						)						)
      tags_dataset.to_json(os.path.join(UpperCAmelCase      ,'pipeline_tags.json'						)						)

      if commit_sha is not None:
         A__       =     (
             F"""Update with commit {commit_sha}\n\nSee: """
             F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
         )
      else:
         A__       =     'Update'

      upload_folder(
          repo_id='huggingface/transformers-metadata'      ,folder_path=UpperCAmelCase      ,repo_type='dataset'      ,token=UpperCAmelCase      ,commit_message=UpperCAmelCase      ,)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   A__       =     transformers_module.pipelines.SUPPORTED_TASKS
   A__       =     []
   for key in pipeline_tasks:
      if key not in in_table:
         A__       =     pipeline_tasks[key]['pt']
         if isinstance(UpperCAmelCase      ,(list, tuple)						):
            A__       =     model[0]
         A__       =     model.__name__
         if model not in in_table.values():
            missing.append(UpperCAmelCase						)

   if len(UpperCAmelCase						) > 0:
      A__       =     ', '.join(UpperCAmelCase						)
      raise ValueError(
          'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
          F"""`utils/update_metadata.py`: {msg}. Please add them!"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
    parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
    parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
    lowerCAmelCase_     						=				parser.parse_args()

    if args.check_only:
        check_pipeline_tags()
    else:
        update_metadata(args.token, args.commit_sha)


